From e14bae345b5009cf65c49d573517176bda7d69f4 Mon Sep 17 00:00:00 2001 From: Tom Brown Date: Wed, 14 Feb 2024 18:15:18 +0100 Subject: [PATCH] new script to interpolate industry sector ratios today to tomorrow For each country we gradually switch industry processes from today's specific energy carrier usage per ton material output to the best-in-class energy consumption of tomorrow in the industry_sector_ratios.csv. This is done on a per-country basis. The ratio of today to tomorrow's energy consumption is set with the config["industry"]["sector_ratios_fraction_future"] parameter. --- config/config.default.yaml | 8 ++ rules/build_sector.smk | 26 ++++++- ...build_industrial_energy_demand_per_node.py | 21 +++-- ...ild_industry_sector_ratios_intermediate.py | 77 +++++++++++++++++++ 4 files changed, 123 insertions(+), 9 deletions(-) create mode 100644 scripts/build_industry_sector_ratios_intermediate.py diff --git a/config/config.default.yaml b/config/config.default.yaml index 79ca890d..0f28ee93 100644 --- a/config/config.default.yaml +++ b/config/config.default.yaml @@ -636,6 +636,14 @@ industry: 2040: 0.12 2045: 0.16 2050: 0.20 + sector_ratios_fraction_future: + 2020: 0.0 + 2025: 0.1 + 2030: 0.3 + 2035: 0.5 + 2040: 0.7 + 2045: 0.9 + 2050: 1.0 basic_chemicals_without_NH3_production_today: 69. #Mt/a, = 86 Mtethylene-equiv - 17 MtNH3 HVC_production_today: 52. MWh_elec_per_tHVC_mechanical_recycling: 0.547 diff --git a/rules/build_sector.smk b/rules/build_sector.smk index f50432d6..bec9aa7a 100644 --- a/rules/build_sector.smk +++ b/rules/build_sector.smk @@ -433,6 +433,30 @@ rule build_industry_sector_ratios: "../scripts/build_industry_sector_ratios.py" +rule build_industry_sector_ratios_intermediate: + params: + industry=config["industry"], + input: + industry_sector_ratios=RESOURCES + "industry_sector_ratios.csv", + industrial_energy_demand_per_country_today=RESOURCES + + "industrial_energy_demand_per_country_today.csv", + industrial_production_per_country=RESOURCES + + "industrial_production_per_country.csv", + output: + industry_sector_ratios=RESOURCES + "industry_sector_ratios_{planning_horizons}.csv", + threads: 1 + resources: + mem_mb=1000, + log: + LOGS + "build_industry_sector_ratios_{planning_horizons}.log", + benchmark: + BENCHMARKS + "build_industry_sector_ratios_{planning_horizons}" + conda: + "../envs/environment.yaml" + script: + "../scripts/build_industry_sector_ratios_intermediate.py" + + rule build_industrial_production_per_country: params: industry=config["industry"], @@ -535,7 +559,7 @@ rule build_industrial_production_per_node: rule build_industrial_energy_demand_per_node: input: - industry_sector_ratios=RESOURCES + "industry_sector_ratios.csv", + industry_sector_ratios=RESOURCES + "industry_sector_ratios_{planning_horizons}.csv", industrial_production_per_node=RESOURCES + "industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv", industrial_energy_demand_per_node_today=RESOURCES diff --git a/scripts/build_industrial_energy_demand_per_node.py b/scripts/build_industrial_energy_demand_per_node.py index 55c10c5d..84f8679a 100644 --- a/scripts/build_industrial_energy_demand_per_node.py +++ b/scripts/build_industrial_energy_demand_per_node.py @@ -19,23 +19,28 @@ if __name__ == "__main__": planning_horizons=2030, ) - # import EU ratios df as csv + # import ratios fn = snakemake.input.industry_sector_ratios - industry_sector_ratios = pd.read_csv(fn, index_col=0) + sector_ratios = pd.read_csv(fn, + header=[0,1], + index_col=0) - # material demand per node and industry (kton/a) + # material demand per node and industry (Mton/a) fn = snakemake.input.industrial_production_per_node - nodal_production = pd.read_csv(fn, index_col=0) + nodal_production = pd.read_csv(fn, index_col=0) / 1e3 # energy demand today to get current electricity fn = snakemake.input.industrial_energy_demand_per_node_today nodal_today = pd.read_csv(fn, index_col=0) - # final energy consumption per node and industry (TWh/a) - nodal_df = nodal_production.dot(industry_sector_ratios.T) + nodal_sector_ratios = pd.concat({node: sector_ratios[node[:2]] for node in nodal_production.index}, + axis=1) - # convert GWh to TWh and ktCO2 to MtCO2 - nodal_df *= 0.001 + nodal_production_stacked = nodal_production.stack() + nodal_production_stacked.index.names = [None,None] + + # final energy consumption per node and industry (TWh/a) + nodal_df = (nodal_sector_ratios.multiply(nodal_production_stacked)).T.groupby(level=0).sum() rename_sectors = { "elec": "electricity", diff --git a/scripts/build_industry_sector_ratios_intermediate.py b/scripts/build_industry_sector_ratios_intermediate.py new file mode 100644 index 00000000..86f88218 --- /dev/null +++ b/scripts/build_industry_sector_ratios_intermediate.py @@ -0,0 +1,77 @@ +# -*- coding: utf-8 -*- +# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors +# +# SPDX-License-Identifier: MIT +""" +Build specific energy consumption by carrier and industries and by country, +that interpolates between the current average energy consumption (from 2015-2020) +and the ideal future best-in-class consumption. +""" + +import pandas as pd + +from prepare_sector_network import get + +def build_industry_sector_ratios_intermediate(): + + # in TWh/a + demand = pd.read_csv(snakemake.input.industrial_energy_demand_per_country_today, + header=[0,1], + index_col=0) + + # in Mt/a + production = pd.read_csv(snakemake.input.industrial_production_per_country, + index_col=0) / 1e3 + production = production.unstack().swaplevel() + + # in MWh/t + future_sector_ratios = pd.read_csv(snakemake.input.industry_sector_ratios, + index_col=0) + + production.index.names = [None,None] + + today_sector_ratios = demand.div(production, axis=1) + + today_sector_ratios.drop(columns=today_sector_ratios.columns[today_sector_ratios.isna().all()], + inplace=True) + + rename = pd.Series(today_sector_ratios.index, + today_sector_ratios.index) + rename["waste"] = "biomass" + rename["electricity"] = "elec" + rename["solid"] = "coke" + rename["gas"] = "methane" + rename["other"] = "biomass" + rename["liquid"] = "naphtha" + + today_sector_ratios.rename(rename, + inplace=True) + + + fraction_future = get(params["sector_ratios_fraction_future"], year) + + intermediate_sector_ratios = {} + + for ct in today_sector_ratios.columns.unique(level=0): + + intermediate_sector_ratio = future_sector_ratios.copy() + + intermediate_sector_ratio.loc[today_sector_ratios[ct].index,today_sector_ratios[ct].columns] = (fraction_future*intermediate_sector_ratio.loc[today_sector_ratios[ct].index,today_sector_ratios[ct].columns] + + (1 - fraction_future)*today_sector_ratios[ct]) + intermediate_sector_ratios[ct] = intermediate_sector_ratio + + intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1) + + intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios) + +if __name__ == "__main__": + if "snakemake" not in globals(): + from _helpers import mock_snakemake + + snakemake = mock_snakemake("build_industry_sector_ratios_intermediate") + + year = int(snakemake.wildcards.planning_horizons[-4:]) + + params = snakemake.params.industry + + build_industry_sector_ratios_intermediate()